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Single-Cell Gene Expression Analysis

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Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis.

Frontiers in immunology
BACKGROUND: The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANopto...

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

PloS one
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains ...

Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.)
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...

Transfer learning enables predictions in network biology.

Nature
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Briefings in bioinformatics
Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurement...

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

International journal of molecular sciences
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designe...

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq ...

Methods for cell-type annotation on scRNA-seq data: A recent overview.

Journal of bioinformatics and computational biology
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in sin...

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Briefings in bioinformatics
Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering al...

scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

Briefings in bioinformatics
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by s...